*
 * <p>When chaining together a graph of asynchronous operations, you will often find it easier to
 * use a framework. Frameworks automate the process, often adding features like monitoring,
 * debugging, and cancellation. Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>
 * </ul>
 *
 * <h4>{@link java.util.concurrent.CompletableFuture} / {@link java.util.concurrent.CompletionStage}

For a quick installation, docker-compose reference configs are also available on the [Keycloak GitHub](https://github.com/keycloak/keycloak-containers/tree/main/docker-compose-examples).

### Configure Keycloak Realm

- Go to Clients
  - Click on account
    - Settings
    - Change `Access Type` to `confidential`.
    - Save
  - Click on credentials tab

 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.
 * Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>
 * </ul>
 *
 * <p>The main purpose of {@link #addListener addListener} is to support this chaining. You will

/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

///

### About `**user_in.dict()` { #about-user-in-dict }

 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.
 * Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>
 * </ul>
 *
 * <p>The main purpose of {@link #addListener addListener} is to support this chaining. You will

import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.RangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.SignificantTermsAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.TermsAggregationBuilder;

Mas você também pode explorar os benefícios do paralelismo e multiprocessamento (tendo múltiplos processadores rodando em paralelo) para trabalhos **limitados por CPU** como aqueles em sistemas de Machine Learning.

Isso, somado ao simples fato que Python é a principal linguagem para **Data Science**, Machine Learning e especialmente Deep Learning, faz do FastAPI uma ótima escolha para APIs web e aplicações com Data Science / Machine Learning (entre muitas outras).

In all the docs there are examples compatible with each version of Python (when there's a difference).

For example "**Python 3.6+**" means it's compatible with Python 3.6 or above (including 3.7, 3.8, 3.9, 3.10, etc). And "**Python 3.9+**" means it's compatible with Python 3.9 or above (including 3.10, etc).

- Improve CPU sample sanitization in HPA by taking metric's freshness into account. ([#68068](https://github.com/kubernetes/kubernetes/pull/68068), [@krzysztof-jastrzebski](https://github.com/krzysztof-jastrzebski))

        print(statsDetails['BytesScanned'])
        print("Stats details bytesProcessed: ")
        print(statsDetails['BytesProcessed'])
```

## 4. Run the Program

Upload a sample dataset to MinIO using the following commands.

```sh
curl "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_TotalPopulationBySex.csv" > TotalPopulation.csv
mc mb myminio/mycsvbucket